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Second-Order Disjoint Factor Analysis

Author

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  • Carlo Cavicchia

    (Erasmus University Rotterdam)

  • Maurizio Vichi

    (University of Rome La Sapienza)

Abstract

Hierarchical models are often considered to measure latent concepts defining nested sets of manifest variables. Therefore, by supposing a hierarchical relationship among manifest variables, the general latent concept can be represented by a tree structure where each internal node represents a specific order of abstraction for the latent concept measured. In this paper, we propose a new latent factor model called second-order disjoint factor analysis in order to model an unknown hierarchical structure of the manifest variables with two orders. This is a second-order factor analysis, which—respect to the second-order confirmatory factor analysis—is exploratory, nested and estimated simultaneously by maximum likelihood method. Each subset of manifest variables is modeled to be internally consistent and reliable, that is, manifest variables related to a factor measure “consistently” a unique theoretical construct. This feature implies that manifest variables are positively correlated with the related factor and, therefore, the associated factor loadings are constrained to be nonnegative. A cyclic block coordinate descent algorithm is proposed to maximize the likelihood. We present a simulation study that investigates the ability to get reliable factors. Furthermore, the new model is applied to identify the underlying factors of well-being showing the characteristics of the new methodology. A final discussion completes the paper.

Suggested Citation

  • Carlo Cavicchia & Maurizio Vichi, 2022. "Second-Order Disjoint Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 289-309, March.
    • Handle: RePEc:spr:psycho:v:87:y:2022:i:1:d:10.1007_s11336-021-09799-6
    DOI: 10.1007/s11336-021-09799-6
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    References listed on IDEAS

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    Cited by:

    1. Chi Yuan Chen, 2023. "Are Professors Satisfied With Their Jobs? The Factors That Influence Professors’ Job Satisfaction," SAGE Open, , vol. 13(3), pages 21582440231, July.

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